Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation

• URL: http://arxiv.org/abs/2604.14726v2
• Date: Tue, 21 Apr 2026 03:27:44 GMT
• Title: Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptation
• Abstract summary: DyMETER is a dynamic concept adaptation framework for online anomaly detection.<n>It unifies on-the-fly parameter shifting and dynamic thresholding within a single online paradigm.<n>DyMETER significantly outperforms existing OAD approaches across a wide spectrum of application scenarios.
• Score: 31.430600150357886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Online anomaly detection (OAD) plays a pivotal role in real-time analytics and decision-making for evolving data streams. However, existing methods often rely on costly retraining and rigid decision boundaries, limiting their ability to adapt both effectively and efficiently to concept drift in dynamic environments. To address these challenges, we propose DyMETER, a dynamic concept adaptation framework for OAD that unifies on-the-fly parameter shifting and dynamic thresholding within a single online paradigm. DyMETER first learns a static detector on historical data to capture recurring central concepts, and then transitions to a dynamic mode to adapt to new concepts as drift occurs. Specifically, DyMETER employs a novel dynamic concept adaptation mechanism that leverages a hypernetwork to generate instance-aware parameter shifts for the static detector, thereby enabling efficient and effective adaptation without retraining or fine-tuning. To achieve robust and interpretable adaptation, DyMETER introduces a lightweight evolution controller to estimate instance-level concept uncertainty for adaptive updates. Further, DyMETER employs a dynamic threshold optimization module to adaptively recalibrates the decision boundary by maintaining a candidate window of uncertain samples, which ensures continuous alignment with evolving concepts. Extensive experiments demonstrate that DyMETER significantly outperforms existing OAD approaches across a wide spectrum of application scenarios.

Related papers

• DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models [63.58458696122293]
  Continual learning enables vision-language models to accumulate knowledge and adapt to evolving tasks without retraining from scratch.<n>Most existing methods rely on fixed architectures with statically allocated parameters, which limits adaptation to new domains and aggravates catastrophic forgetting.<n>We propose DIMoE-Adapters, a Dynamic Incremental Mixture-of-Experts Adapters framework that introduces a dynamic expert evolution paradigm to balance stability and plasticity.
  arXiv  Detail & Related papers  (2026-05-08T09:32:05Z)
• Training-Free Diffusion-Driven Modeling of Pareto Set Evolution for Dynamic Multiobjective Optimization [15.780107358426745]
  This paper proposes DD-DMOEA, a training-free diffusion-based dynamic response mechanism for DMOPs.<n>A knee-point-based auxiliary strategy is used to specify the target region in the new environment.<n>Experiments on the CEC2018 dynamic multiobjective benchmarks show that DD-DMOEA achieves competitive or better convergence-diversity performance.
  arXiv  Detail & Related papers  (2026-03-23T11:09:58Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering [49.212940215720884]
  We propose a steering framework that generates sample-level interference from user data and injects it into the model's forward pass for personalized adaptation.<n>Our method significantly enhances personalization performance in fast-shifting environments while maintaining robustness across varying interaction modes and context lengths.
  arXiv  Detail & Related papers  (2025-10-31T06:01:04Z)
• TC-LoRA: Temporally Modulated Conditional LoRA for Adaptive Diffusion Control [15.019368747393862]
  Current controllable diffusion models rely on fixed architectures that modify intermediate activations to inject guidance conditioned on a new modality.<n>We introduce TC-LoRA, a new paradigm that enables dynamic, context-aware control by conditioning the model's weights directly.
  arXiv  Detail & Related papers  (2025-10-10T17:13:02Z)
• EKPC: Elastic Knowledge Preservation and Compensation for Class-Incremental Learning [53.88000987041739]
  Class-Incremental Learning (CIL) aims to enable AI models to continuously learn from sequentially arriving data of different classes over time.<n>We propose the Elastic Knowledge Preservation and Compensation (EKPC) method, integrating Importance-aware importance Regularization (IPR) and Trainable Semantic Drift Compensation (TSDC) for CIL.
  arXiv  Detail & Related papers  (2025-06-14T05:19:58Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection [25.022228143354123]
  Real-time analytics and decision-making require online anomaly detection to handle drifts in data streams efficiently and effectively. Existing approaches are often constrained by their limited detection capacity and slow adaptation to evolving data streams. We introduce METER, a novel dynamic concept adaptation framework that introduces a new paradigm for OAD.
  arXiv  Detail & Related papers  (2023-12-28T05:09:31Z)
• ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation [48.039156140237615]
  A Continual Test-Time Adaptation task is proposed to adapt the pre-trained model to continually changing target domains. We design a Visual Domain Adapter (ViDA) for CTTA, explicitly handling both domain-specific and domain-shared knowledge. Our proposed method achieves state-of-the-art performance in both classification and segmentation CTTA tasks.
  arXiv  Detail & Related papers  (2023-06-07T11:18:53Z)
• DA-LSTM: A Dynamic Drift-Adaptive Learning Framework for Interval Load Forecasting with LSTM Networks [1.3342521220589318]
  A drift magnitude threshold should be defined to design change detection methods to identify drifts. We propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models.
  arXiv  Detail & Related papers  (2023-05-15T16:26:03Z)
• Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control [46.81433026280051]
  We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
  arXiv  Detail & Related papers  (2022-10-23T00:45:05Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.